According to the Harvard Business Review, approximately eighty-five percent of corporate financial performance is caused by factors external to the business, as opposed to internal actions taken by the company. In a 2009 paper, Gartner, Inc. predicted that, by 2012, more than thirty-five percent of the top 5,000 global companies would regularly fail to make insightful decisions about significant changes in their business markets. Despite this enormous influence exerted by external factors on a company's operations, the current state of the art leaves the companies with little or no systematic and fluid method for understanding how external market forces impact their businesses. When, through much effort, a company does uncover some external factors that may be a driving force for a facet of its operations, it still lacks the ability to leverage this information for strategic planning. As a result, most corporations are not adequately prepared to address changes in economic conditions as they occur resulting in lost opportunity or degraded company performance.
Many companies today have implemented business intelligence solutions to leverage information technology and computing power to provide historical and current views of internal business operations. Business intelligence solutions enable companies to review large quantities of data with respect to a variety of internal metrics and processes. They are used to report on these data so that decision makers can, for example, perform data mining to identify and analyze process inefficiencies, areas of weakness and strength, divisional and product performance, and management performance. The data collected for any given company varies greatly with regard to, for instance, business sophistication, size, industry standards and relevant metrics, competitive considerations, and technical barriers.
Firms routinely monitor financial metrics internal to the business for performance evaluation. The financial metrics can include, for example, sales, profits, and costs, generally. Monitoring and analyzing these many internal metrics can be the key to a firm's long-term success. For example, in some businesses, the cost of goods sold for certain key product lines are vital to the year-to-year performance of the company. Knowing why the cost of goods sold has changed in the past and analyzing its historical trends will ensure that the business leaders are better equipped to manage the company successfully. The ability to predict the future movement of financial metrics is even more valuable to a company.
Macroeconomic metrics, or economic metrics, are statistical measurements of an economy's characteristics. They can be national economy metrics, international economy metrics, industry-specific metrics at various levels, or the like. Economic metrics are used to analyze economic performance and conduct predictive forecasting of the future performance of some other portion of the economy. Economic metrics are generated, produced, cataloged, and published by a plethora of firms, with many key metrics originating with one or more of several government offices such as the Bureau of Labor Statistics, or other private firms such as the National Bureau of Economic Research.
Economic metrics have historically been used in the field of econometrics as a means for explaining historical trends and events, and as predictors of future economic performance. In furtherance of the latter goal, economic metrics are often compared against each other to determine whether one economic metric can be considered to be an indicator of the other economic metric. Economic indicators can be predictive indicators for other economic indicators, or for the economy as a whole. For example, stock market indexes are considered a leading indicator of the general state of the economy: declines in the stock markets signal an upcoming economic downturn, while consistent gains often predate periods of economic improvement.
Economists and corporate financial departments compare the historical values of two economic metrics and statistically analyze them for evidence that one metric is an indicator for the other metric. If a metric is found to be an indicating metric, it can be classified as one of three types of indicating metrics: leading, lagging, or coincident. A leading indicator is an economic metric whose movement is statistically followed by the movement of a second economic metric sometime in the future. Conversely, a lagging indicator is an economic metric whose movement statistically follows the movement of a second economic metric; it changes consistently with the movement of the second metric and before the second metric. Finally, coincident indicators are found when two economic metrics change at approximately the same time.
The change observed in an economic metric is also classified according to its direction of change relative to the economic indicator that it is being measured against. When the economic metric changes in the same direction as the indicator, the relationship is said to be pro-cyclic. When the change is in the opposite direction as the indicator, the relationship is said to be counter-cyclic. Because no two metrics will be fully pro-cyclic or counter-cyclic, it is also possible that a metric and an indicator can be acyclic—i.e., the metric exhibits both pro-cyclic and counter-cyclic movement with respect to the indicator.
More recently, companies have begun to analyze economic metrics to determine if there are indicator relationships between macroeconomic metrics and the company's own internal financial metrics. For example, a company that produces treated lumber may be interested in determining if United States housing starts is an indicator for the internal sales metric. That is, if housing starts begin to climb, can the company expect a climb in sales and production, and if so, how much and when?
The current systems and methods designed to answer these questions pose several problems that are not solved by the prior art. First, data aggregation is a difficult and time-consuming task. Certain macroeconomic metric data sets, such as United States housing starts, are freely available from various sources. New, updated figures are released according to a set periodic schedule. The updated data sets must be obtained and imported into analysis software, such as Microsoft Excel, in order to compare the metrics to determine whether an indicator relationship may be found. Internal company metrics must also be imported into the software to begin the comparison.
The current methods of data aggregation suffer from the problem of disuniformity; that is, the external metrics and internal metrics must be converted to a similar format suitable for conducting indicator analyses. To achieve a comprehensive analysis of any two metrics, it is often desirable to compare not only actual values over time, but also statistical measurements of change, such as month-over-month percent change, year-over-year percent change, and three-month moving averages, for instance. Each new analysis thus creates the need to perform the time-consuming data preparation operations of homogenizing the data sets and calculating all desirable statistical permutations, before conducting an actual analysis.
Some partial solutions have been attempted, but none achieve the goal of quickly preparing data sets for analyses without the need to prepare the data. For example, some companies maintain subscriptions to services offered by the likes of IHS, Inc., Bloomberg Government, Moody's Investor Services, and Thomson and Reuters Corporation. For some macroeconomic metric services, analysis software add-ins are available to fetch and import data at a user's request. These solutions only serve to create a patchwork of data sets spread across a multitude of files. Furthermore, the data sets are not updated automatically and in close temporal proximity to the actual release date. Rather, the data update operation is dependent on a user knowing the release schedule and manually activating the update function.
While, over time, some firms will develop know-how with regard to which external metrics to analyze for insight into their own internal operations, the ability to analyze a large number of external metrics currently requires a significant time commitment. Each iterative step of an analysis essentially requires an analyst to import, convert, and perform statistical permutation operations on the desired metric data sets. The analysis must then be carried out—graphs, charts, and results must be created for each iteration. Therefore, comparing two metrics can place a significant demand on the analyst's time, consequently restricting the number of metric pairs that can be compared. The need for a system and method that greatly increases the speed with which one can perform the necessary comparisons would greatly enhance a firm's ability to obtain knowledge of how external factors affect its operations, thereby heightening the potential for increasing business efficiency and profitability.
Although the current methods of data aggregation and econometric analysis are inefficient, the insight gleaned from those methods is still valuable to the successful operation of a business. Therefore, many businesses conduct such analyses and gain insight into their internal operations due to external driving forces. Some of the same inefficiencies that plague the aggregation of data and the subsequently analysis thereof present further difficulties to companies. If a firm is able to determine a set of external metrics to watch, it continues to remain difficult to act accordingly when the external metrics change over time. For example, the employee in charge of monitoring a particular external metric must manually update the data used in a report to determine if the new information suggests a strategic change in business operations. While some data providers will alert subscribers that updated data sets have been made available, the employee is still required to go through multiple steps to update the relevant reports.
Therefore, while some companies currently have begun to perform econometric analyses to determine which external factors affect internal business operations, and to what extent, the exists a need in the art for more efficient and robust systems and methods for greatly increasing a firm's ability to analyze, monitor, and react to changing external environments. Developments in the field have shown that businesses routinely underestimate or ignore the insight into internal business operations that can be gleaned from external metrics. The present invention seeks to remedy this deficiency and enhance decision-making by providing a unique system and method for improving business performance forecasting and econometric analyses.